RT Journal Article
T1 Combining wavelet transform with convolutional neural networks for hypoglycemia events prediction from CGM data
A1 Alvarado, Jorge
A1 Velasco Cabo, José Manuel
A1 Chávez de la O, Francisco
A1 Fernández de Vega, Francisco
A1 Hidalgo Pérez, José Ignacio
AB Estimating future blood glucose levels is an essential and challenging task for people with diabetes. It must be carried out based on variables such as current glucose, carbohydrate intake, physical activity, and insulin dosing. Accurate estimation is essential to maintain glucose values in a healthy range and avoid dangerous events of low glucose levels (hypoglycemia) and extremely high glucose values (hyperglycemia). Those situations maintained in time can cause not only permanent long-term damage but also short-term complications and even the death of the person. This paper proposes a new method to predict and detect hypoglycemic events over a 24-h time horizon. The technique combines applying the wavelet transform to glucose time series and deep learning convolutional neural networks. We have experimented with real data collected from 20 different people with type 1 diabetes. Our technique can also be applied to predict hyperglycemia. We incorporate a data augmentation technique consisting of a rolling windows system that improves the accuracy of the prediction. The uncertainty of the data is considered by the addition of controlled noise. The results show that the predictions obtained are accurate (higher than 88% of accuracy, sensitivity, specificity, and precision), confirming the effectiveness of the proposed method.
PB Elsevier
SN 0169-7439
YR 2023
FD 2023-12-15
LK https://hdl.handle.net/20.500.14352/117453
UL https://hdl.handle.net/20.500.14352/117453
LA eng
NO Jorge Alvarado, J. Manuel Velasco, Francisco Chavez, Francisco Fernández-de-Vega, J. Ignacio Hidalgo, Combining wavelet transform with convolutional neural networks for hypoglycemia events prediction from CGM data, Chemometrics and Intelligent Laboratory Systems, Volume 243, 2023, 105017, ISSN 0169-7439, https://doi.org/10.1016/j.chemolab.2023.105017. (https://www.sciencedirect.com/science/article/pii/S0169743923002678) 
NO Ministerio de Ciencia, Innovación y Universidades(España)
NO Junta de Extremadura
NO Unión Europea
NO Fundación Eugenio Rodriíguez Pascual
NO Comunidad de Madrid
DS Docta Complutense
RD 17 abr 2025